Enhancing social media Integrity a Machine learning based rumor identification system utilizing CNN for accurate real time tweet analysis

Modern cyber-attacks grow tougher that motivates the need for advanced protection methods. A real-time attack detection system operates through analysis of SIP signals by implementing CNN-based approaches according to the concept. The automated traffic analysis of the system uses a detection mechanism which detects potential attacks with both precision and speed. The CNN model uses network analysis to generate threat-based protection better than traditional signature approaches that need manual rulemaking. A dynamic real-time streaming system operates within the system framework to process SIP signals in real-time. The proposed detection approach succeeds in security tests which establishes exceptional results while reducing false warning occurrences. The approach works through deep learning techniques that promote automatic real-time attack detection which functions with high efficiency.